Abstract:
In one embodiment, a device in a serial network de-multiplexes a stream of traffic in the serial network into a plurality of data streams. The device determines that data from a particular data stream should be reported to an entity external to the serial network based on an event indicated by the data from the particular data stream. The device quantizes the data from the particular data stream. The device applies compression to the quantized data to form a compressed representation of the particular data stream. The applied compression is selected based on a data type associated with the data. The device sends a compressed representation of the particular data stream to the external entity as Internet Protocol (IP) traffic.
Abstract:
In one embodiment, a processor of a vehicle detects a difference between a physical characteristic of the vehicle predicted by a first machine learning-based model and a physical characteristic of the vehicle indicated by telemetry data generated by a sub-system of the vehicle. The processor forms a packet payload of an update packet indicative of the detected difference, based in part on a relevancy of the physical characteristic to the first machine learning-based model. The processor applies a synchronization strategy to the update packet, to synchronize the update packet with a second machine learning-based model executed by a receiver. The processor sends the update packet to the receiver via a network, to update the second machine learning-based model.
Abstract:
In one embodiment, a network device connected to an Internet Protocol (IP) network and a serial network scans an infrastructure of the serial network. Based on the scanning, the network device can determine one or more serial endpoints within the serial network infrastructure, and may then allocate an IP address to each of the one or more serial endpoints. The network device may then map received IP network traffic into serial protocol commands on the serial network for a destination serial endpoint having an allocated IP address corresponding to a destination IP address of the received IP network traffic, and may also bridge data present on the serial network from a sourcing serial endpoint into an IP message on the IP network with an indication of a corresponding allocated IP address of the sourcing serial endpoint, accordingly.
Abstract:
A method provided in a network including edge devices to collect data from data producers connected to the edge devices and to communicate with cloud-based prosumers connected with the edge devices. Data analytics tasks are identified. The data analytics tasks are used to process data collected from a data producer among the data producers to produce a result for consumption by one or more of the cloud-based prosumers. For each data analytics task it is determined whether a computational complexity of the data analytics task is less than or equal to a predetermined computational complexity. Each data analytics task determined to have a computational complexity less than or equal to the predetermined computational complexity is assigned to an edge device among the edge devices. Each data analytics task determined to have a computational complexity that exceeds the predetermined computational complexity is assigned to a prosumer among the prosumers.
Abstract:
In one embodiment, a fog computing-based fueling kiosk forms a fueling connection with a vehicle and a direct network connection between the kiosk and a gateway for a network of the vehicle. The fueling kiosk provides energy to the vehicle via the fueling connection and receives, via the network connection with the gateway for the network of the vehicle, operational data from the network of the vehicle, while providing the energy to the vehicle via the fueling connection. The fueling kiosk performs an analysis of the received operational data from the vehicle and provides a result of the performed analysis to a remote device.
Abstract:
Information describing a rule to be applied to a traffic stream is received at an edge network device. The traffic stream is received at the edge network device. A schema is applied to the traffic stream at the edge network device. It is determined that a rule triggering condition has been met. The rule is applied to the traffic stream, at the edge network device, in response to the rule triggering condition having been met. At least one of determining that the rule triggering event has taken place or applying the rule is performed based on the applied schema.
Abstract:
In one embodiment, a network device connected to an Internet Protocol (IP) network and a serial network scans an infrastructure of the serial network. Based on the scanning, the network device can determine one or more serial endpoints within the serial network infrastructure, and may then allocate an IP address to each of the one or more serial endpoints. The network device may then map received IP network traffic into serial protocol commands on the serial network for a destination serial endpoint having an allocated IP address corresponding to a destination IP address of the received IP network traffic, and may also bridge data present on the serial network from a sourcing serial endpoint into an IP message on the IP network with an indication of a corresponding allocated IP address of the sourcing serial endpoint, accordingly.
Abstract:
A method provided in a network including edge devices to collect data from data producers connected to the edge devices and to communicate with cloud-based prosumers connected with the edge devices. Data analytics tasks are identified. The data analytics tasks are used to process data collected from a data producer among the data producers to produce a result for consumption by one or more of the cloud-based prosumers. For each data analytics task it is determined whether a computational complexity of the data analytics task is less than or equal to a predetermined computational complexity. Each data analytics task determined to have a computational complexity less than or equal to the predetermined computational complexity is assigned to an edge device among the edge devices. Each data analytics task determined to have a computational complexity that exceeds the predetermined computational complexity is assigned to a prosumer among the prosumers.
Abstract:
Information describing a rule to be applied to a traffic stream is received at an edge network device. The traffic stream is received at the edge network device. A preliminary data analysis of the traffic stream is performed at the edge network device in accordance with the rule. A determination is made that further analysis of the traffic stream should be performed from a result of the preliminary analysis. The traffic stream data is sent to another network device for further analysis.
Abstract:
An example method is provided in one example embodiment and includes receiving a mobility event message for a first user equipment; determining demographic information for a first subscriber associated with the first user equipment; determining a location of the first subscriber in relation to a first network domain; identifying one or more advertising domains in relation to the location of the first subscriber based on the first network domain; determining a modeling function based on the mobility event message and the location of the first subscriber in relation to the identified advertising domains; updating, based on the modeling function, one or more demographic models for each of the identified one or more advertising domains using the demographic information for the first subscriber; and following the updating, calculating demographic information for all subscribers for each of the updated one or more demographic models for the identified advertising domains.